%matplotlib inline
# import network warper demo
import warp_demo_3 as warper

# a necessary library for writing out our transform rules mathematically
from sympy import symbols, lambdify, sin, cos, tanh, exp, log, Max, Piecewise, And
from ipywidgets import interact
import sys

To transform this notebook into an interactive html page - with the animations imbedded - from the dir type

jupyter nbconvert scratchpad.ipynb

This is how scratchpad.html (also in this dir) was generated.

# our home-made warper 
demo = warper.warp_demo()

# make instance of warper
x1,x2 = symbols('x1 x2')

# define your own rules here using elementary functions like sin, cos, tanh, sinc, or any polynomial combination of x1 and x2
rule1 = x1*x1 - 4
rule2 = x2*x2 - 4

# these two lines feed the transformation rules into the warping function and produce a toy dataset based on them
demo.define_rule(rule1,rule2)
demo.make_pts()

# this line creates an instance of the slider -  if you do not have bokeh installed you can use the demo1.transformation_slider - this uses matplotlib as the backend (and is considerably slower)
demo.transformation_slider()

rendering JS animation, this can take several minutes...

# our home-made warper 
demo2 = warper.warp_demo()

# make instance of warper
x1,x2 = symbols('x1 x2')

# define your own rules here using elementary functions like sin, cos, tanh, sinc, or any polynomial combination of x1 and x2
rule1 = tanh(x1 + x2 + tanh(x1))
rule2 = tanh(0.1*x1)

# these two lines feed the transformation rules into the warping function and produce a toy dataset based on them
demo2.define_rule(rule1,rule2)
demo2.make_pts()

# this line creates an instance of the slider -  if you do not have bokeh installed you can use the demo1.transformation_slider - this uses matplotlib as the backend (and is considerably slower)
demo2.transformation_slider()

rendering JS animation, this can take several minutes...